The paper presents a segmentation algorithm for offline cursive handwriting recognition, focusing on the ideal distance method to accurately segment letters from handwritten text. Using a dataset of 999 handwriting images, the proposed approach achieved a 97% success rate in recognition by determining segmentation points while avoiding cutting through letter sections. The methodology involves preprocessing, morphological operations, and feature extraction using support vector machines to achieve effective handwriting recognition.

1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 7, No. 5, October 2017, pp. 2863~2872
ISSN: 2088-8708, DOI: 10.11591/ijece.v7i5.pp2863-2872  2863
Journal homepage: http://iaesjournal.com/online/index.php/IJECE
Cursive Handwriting Segmentation Using Ideal Distance
Approach
Fitrianingsih1
, Sarifuddin Madenda2
, Ernastuti3
, Suryarini Widodo4
, Rodiah5
1, 3, 4, 5
Departement of Informatics, Gunadarma University
2
Doctoral Program in Information Technology, Gunadarma University
Article Info ABSTRACT
Article history:
Received Jan 11, 2017
Revised Apr 7, 2017
Accepted Jun 11, 2017
Offline cursive handwriting becomes a major challenge due to the huge
amount of handwriting varieties such as slant handwriting, space between
words, the size and direction of the letter, the style of writing the letter and
handwriting with contour similarity on some letters. There are some steps for
recursive handwriting recognition. The steps are preprocessing, morphology,
segmentation, features of letter extraction and recognition. Segmentation is a
crucial process in handwriting recognition since the success of segmentation
step will determine the success level of recognition. This paper proposes a
segmentation algorithm that segment recursive handwriting into letters.
These letters will form words using a method that determine the intersection
cutting point of image recursive handwriting with an ideal image distance.
The ideal distance of recursive handwriting image is an ideal distance
segmentation point in order to avoid the cutting of other letter’s section. The
width and height of images are used to determine the accurate segmentation
point. There were 999 recursive handwriting input images taken from 25
researchers used for this study. The images used are the images obtained
from preprocessing step. Those are the images with slope correction. This
study used Support Vector Machine (SVM) to recognize recursive
handwriting. The experiments show the proposed segmentation algorithm
able to segment the image precisely and have 97% success recognizing the
recursive handwriting.
Keywords:
Cursive
Handwriting
Ideal distance
Offline
Segmentation
Copyright © 2017 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
1. INTRODUCTION
Letter recognition based on handwriting (especially recursive handwriting) has long been identified
as a problem that is difficult to solve by computer. The characteristics of offline handwriting are influenced
by the nature of individual author, hardware and data acquisition [1-3]. In general, the procedures of
recursive offline handwriting recognition start with image acquisition then preprocess the images by
correcting the images that contain slanted letter and document. The next procedure is morphology step
followed by segmentation process to determine the cutting point of feature extraction and recognition. In this
study morphological operation and histogram projection were carried on to explore characteristic of offline
handwriting. Morphological steps were performed to obtain (i) the required text and (ii) identification of
object position that being represented as regions inside the image. The opening operation was performed to
cut off the text objects that connect to one or two other vertex of the text. On the other hand, the operation
closing was conducted to remove the small holes inside the segmented letters. There are two steps in
segmentation procedure, (i) horizontal projection and (ii) vertical projection. In this study, finding the most
critical vertex from characters is an important step before searching for segmentation compiler for characters.

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The important vertexes in this study are the endpoint vertex and the vertex of the branch. Both of the vertexes
will be used as the starting and ending vertex to start the searching step to find the nearest neighbor to
determine the character’s segmentation compiler. From each of the important vertex, this study explores and
investigates all possible character segmentation compilers [4]. From the input data (handwriting images), we
conduct preprocessing procedure using Otsu Method then searching for the possible segmentation zone that
can act as the cutting vertex. To obtain the appropriate segmentation result, Genetic Algorithm plays
important role performing non-linear segmentation [5].
This study exploring vertical letter separation after skeleton methods was performed to count the
amount of pixels in letter that will be used as candidate for point separator. These candidates will be
evaluated based on their distance to each other. The candidates that are in seven pixels distance will be
merged as a separator point. The letters used for implementation were taken using scanner of digital camera.
Right and left contour analysist were carried on since Latin or Roman letter has at least one left contour [6].
Another research were exploring maximum average letter similarity to find the letter’s point for
letter separation step. All letter’s point candidate that will be used as a separator formed a graph that contain
weight of letter similarity. These graphs are used to find the point separator by using average longest path
algorithm. Whereas, the similarity of the letters are employed as graph’s weight. These weights were
classified using SVM. The features that being classified by SVM can be extracted using histograms of
oriented gradients. The oriented graph will form a cycle if 0 weight path of point separator’s candidate was
added to the first candidate. This cycle reduce the difficulties of finding the average longest route to ratio
maximum cycle [6].
2. RESEARCH METHOD
2.1. Data Sets
In this study, the input images were offline handwriting letters which were obtained from local
dataset and IAM [7] with JPG and PNG format. The size of the images are varied with total images are 999
images. The overall images used in this study are the images with recursive handwriting characteristics.
These characteristic contain; (i) normal upright recursive handwriting, (ii) italic letters in overall handwriting
(slope) and (iii) tilting letter or text font (slanting). The examples of the images used in this study can be seen
in Table 1.
Table 1. Experimented Image (Local and IAM Dataset)
Image
Slope
Slant
2.2. Preprocessing
Preprocessing step in this study were performed by corrected the images that have slant and slop
characters. This processing are done using affine 2D transformation. The slope of the document often occur
in image acquisition resulting the handwriting inside document written in tilt position. The characteristic of
recursive handwriting are being influenced by many factors. Some of the factors are (i) the style of each
individual, (ii) the orientation of the handwriting (to determine the status and position of the text, whether the
text is horizontal or vertical) and (iii) whether the handwriting form a certain angle [5]. The image slant
correction can be done by detecting the angle of slant of the original image (). This angle generally ranged
between 0 until 180. The image will be reconstructed till generate horizontal angle of 0. In recursive slant
letter, shearing operation was performed. This operation were carried out by shifting initial image in the
direction of x or y-axis by a certain scale. The shifting of the images were performed by defining the matrix
below [8]:










100
01
01
sh
shy
(1)
where :

3. IJECE ISSN: 2088-8708 
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ysh
=Shearing factor along y-axis
xsh = Shearing factor along x-axis
The result of slanting correction in preprocessing step can be seen in Figure 1.
Figure 1. The result of slanting correction letter
2.3. Morfology
This study used morfologi step of Zhang Suen’s method to perform thinning process. This process is
very crucial since it was the first step before performing the next steps [8]. Thinning will eliminate the outer
most layer of handwriting image pattern leaving only the line that consist of one pixel only. This line is called
skeleton of the image. Each iteration of this method consist of two sub successive iteration that performed on
contour point of image region, assuming a pixel with value one (1) is pixel foreground and pixel with value
zero (0) is the background. An object pixel have 8-neighbor rule. The first iteration will represent Cartesian
coordinate for each element as small squares. The alteration of skeleton in this study were made in binary
form that is performed in one image. If the pixel in the handwriting image have at least one of its four
neighborhoods (p0, p2, p4, p6) with the value less than its each grey level value or the whole eight (8) of its
neighbor is less than their grey value, then its binary value turned into 1 or 0. The second iteration made
during bwmorph instruction. The origin of structuring elements positioned in each of the pixel in the input
image, then the value of structuring elements correspond to the pixel value.
2.4. Histogram
This study used morfologi step of Zhang Suen’s method to perform thinning process. The next step
after performing recursive handwriting thinning morphology process was to obtain the image intensity
information using histogram method. Histogram of a digital image has intensity level ranged between [0, L-
1] with discrete function [9].
kk nrH )( (2)
For recursive image with pixel level 0 – 225, the histogram will draw pixel frequency distribution in
0–225 interval. On the other hand, for the recursive binary image which own 0 and 1 level, the histogram will
draw their frequency distribution on foreground with 1 value. The next step is to add this value vertically.
The process can be seen in Figure 2.
Figure 2. Histogram of vertical image pixels quantity
2.5. Segmentation using Ideal Distance
Segmentation is the process of recursive handwriting partition into individual letter. Before the
partition step performed, it is important to find the segmentation point inside the segmented image. This
segmentation point was taken from the sum of vertical image pixel. The sum was carried out downward until
the bottom of image pixel that has passed through preprocessing step. The image that has passed through
preprocessing step will be used to decide the segmentation point. The foreground that is part of this
handwriting image (the image that has passed through the processing step) has one (1) value, therefore when

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we sum up the pixel vertically, the result is a value that will be used as the segmentation point. Before
examine the segmentation point, it is important to compute the ideal distance of the points in an image. The
ideal recursive handwriting image distance is the ideal replacement of segmentation point in order to avoid
cutting off the section of the letter in an image. As long as the spacing distance is ideal, the segmentation
point cannot be done. On the other hand, after passing through the ideal spacing distance, the segmentation
can be done by examining the amount of vertical pixel. The ideal spacing distance is calculated from the
image width divided by the devisor that follows the width of the image in order to adjust the number of
letters that contained in an image. For example, the width of the image after preprocessing step in Figure 3 is
310 pixels with the ideal spacing distance 38.75. The value 38.75 indicating subsequent to placement
segmentation point, within 38.75 distance pixel is impossible to re-segment the pixel point since the distance
spacing of the letter is already ideal.
Figure 3. Image with 310 x 85 pixels
Figure 4 shows the algorithm proposed in this study to determine tha devisor value dan
segmentation point based on ideal distance between letters.
Figure 4. Flowchart to determine dividers value
dideal = length(x,y)/c
PSP(i) = 1
indeks = 2
temp_i = 1
for i=2 : length(y)
PSP(indeks) = i
Indeks = indeks+1
temp_i = 1
temp_i < (1-
dideal)
length(y)<=1
Start
End
Y
Y
N
N

5. IJECE ISSN: 2088-8708 
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There are some proposed steps to determine the segmentation point of a candidate image in
recursive offline handwriting recognition:
1. Determine the image width(x) of handwriting f(x, y)
2. Create a Possible Segmentation Point (PSP) array to store segmentation point with the first index is the
first horizontal pixel position.
3. Create variable temp_i as pointer checker.
4. Determine the ideal distance using the formula (3) :
c
ylength
dideal
)(
 (3)
where :



















6
),(
;299
8
),(
;499300
12
,(
;500
yxf
cx
yxf
cxx
yxf
cx
px
px
px
ideald = Ideal distance
),( yxf = Recursive handwriting image
x = The Image width
y = The Image height
5. Create iteration starting from the 2nd
pixel untill the width of image pixles, then add each iteration in new
pixel.
6. If the sum of vertical image pixel  1, then we redo the checking. If variabel temp_i  from the pixel
checking possition is reduced with ideald then:
a. The content of PSP array index is the newest position of current iteration
b. Pointer indeks PSP array increase one
c. Pointer temp_i is worth the current iteration position.
2.6. Words Feature Extraction Recognition
The training set used in this study are SVM [9]. After training the classifier using the result of
feature letter by letter extraction from training set, the next step is performing feature image extraction from
the test set. Test set is a set of images that contain the letters to be recognized. The result of this test set is the
letter’s weight and label that will be used for recognition step.
The steps of feature extraction and recursive offline handwriting recognition can be seen below:
1. Load 'trainedClassifier_Final_20.mat' which are the results from the training set.
2. Load the segmented image in img variable then performing feature extraction based on HOG to img. The
result of feature extraction will be stored in testFeatures variable.
3. Performing score prediction to compare the result form feature extraction testFeatures variable with the
score of classifier.
4. The obtained score prediction will be compared with score label of classifier and then will be loaded in
label variable.
The general chart of feature extraction and recursive offline handwriting recognition can be seen in
Figure 5 below.

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Figure 5. Feature extraction and recursive offline handwritting recognition
Table 2 shows the result of feature image extraction with their weights and labels
Table 2. Image, Weight and Label from Feature Extraction
Citra Bobot Label
0.9901 s
0.9908 a
0.9925 i
0.9973 l
0.9901 o
0.9906 r
Start
load
“trainedClassifier
_Final_20.mat”
Counter = 1;
For i = 1 to
img
testFeatures =
extractHOGFeatures(img,”CellSize”,4 4])
[predictedLabels, score] = predict(classifier,
testFeatures);
label(counter) = predictedLabels;
counter = counter + 1
Next 1
I = img ?
end
Y
N

7. IJECE ISSN: 2088-8708 
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3. RESULTS AND ANALYSIS
3.1. Experimental Image
Segmentation process is based on segment point that being applied inside the program. This
segmentation point was obtained from image column that contain vertical pixel less than or equal to one and
cannot be found in ideal distance. For example, Table 3 shows 10 images out of 999 images that were
successfully segmented.
Table 3. Segmentation Based On Ideal Distance
No Original Image Segmented Image
1
2
3
4
5
6
7
8
9
10
The results from segmentation point placement were not always precise and in correct position.
These were due to some characteristic of recursive handwriting that differs with the printed handwriting. The
differences are:
1. There is no empty space between letter in recursive handwriting, therefore the quantity of vertical pixels
in segmentation point is more than or equal to one.
2. There are some letters that have characteristic as segmentation candidates. These letters have letter lines
in which their vertical pixels quantity equal to one. For example U, V, W, N, M, and H.
There were also letters that are not close perfectly, For example the writing of cursive ‘a’ that was
written with imperfect closed writing resulted the loop in letter ‘a’ determined as a cutting point. As a result,
these letters were not detected as segmentation candidate. Figure 6 shows unsuccessful segmentation
candidate.

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Figure 6. Letters with unsuccessful segmentation
Figure 6 shows the unsuccessful image segmentation with 3o slant of letter. Letter L and D were not
correctly segmented because of the amount of vertical pixels between letter L and D ranged on y-axis
between 130 until 150 pixels. The resulted pixels did not meet the conditions less than or equal one. As
consequences, the cutting process cannot be done correctly at this point. The example of image recognition
can be found in Figure 7.
Figure 7. The result of letter recognition
Previous research (Salvi et al, 2012) performed segmentation method on finding the separation point
of the letters using the average of maximum letter’s similarity. All the candidates of separation point will
formed a graph with letter similarity as their weight. The graph employed in their research try to find the
separation point using the longest average route algorithm. This method produced errors in segmentation
process. The errors can be seen in Figure 8. The created graph have the edge from candidate no 2 and all the
route the same as the image cutting based on candidate no 2. The final step of second validation is searching
the longest greedy route from the created graph, resulted over segmentation on handwriting image.

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Figure 8. Greedy algorithm segmentation result
3.2. The Result of System Perfomance
The performance measurement of offline recursive handwriting in this study was computed using
ratio of successfulness and failure. The computation can be done using the formulas below:
%100
Im
__ x
agesTotal
onSegmentatiSuccessful
RatioSuccessThe


 (4)
%100
Im
__ x
agesTotal
tionulSegmentaUnsuccessf
RatioFailureThe


 (5)
The result of successful segmentation ratio and failure based on active image contour in this study
can be seen in Table 4 and Figure 9.
Table 4. Segmentation Performance
No Image Source Success Fail
1 Local Dataset 495 5
2 IAM Dataset 487 12
Total 982 17
%98%100
999
982
____  xonSegmentatiofRatioSuccessThe
%2%100
999
17
____  xonSegmentatiofRatioFailureThe
Figure 9. Segmentation accuracy
4. CONCLUSION
Based on the overall description and experiment’s result, the proposed algorithm able to segment the
offline recursive handwriting images precisely (around 98% accuracy) based on its cutting point, by
calculating the ideal distance from offline recursive handwriting. There were 999 recursive handwriting data.
Unsuccessful segmentation caused from handwriting images that experienced distortion on their y-axis

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(ranged between 130 until 150 pixels). These resulted pixels do not meet the conditions (≤ 1), as
consequences the cutting point on those pixels cannot be perform. The accurate point to cut on segmentation
step will achieve the accuracy of handwriting recognition till 98% of success.
ACKNOWLEDGEMENTS
We are indebted to the experts who have contributed towards development of the template.The
authors would like to acknowledge to Gunadarma University.
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